Market basket analysis of bakery dataset
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Updated
Aug 7, 2024 - Jupyter Notebook
Market basket analysis of bakery dataset
Oat : Hi !
Optimize sales while better understand the customers through Segmentation and Recommendation analysis.
Conducted Market Basket Analysis (MBA) on Amazon product dataset to enhance recommendations. Identified top-selling products and top products in each category using review count. Implemented asso- ciation rule mining for personalized recommendations. Evaluated effectiveness through metrics.
Market basket analysis uses data mining techniques to identify patterns in customer purchase behavior by examining items frequently bought together. This analysis helps retailers optimize product placement, tailor promotions, and improve inventory management, ultimately enhancing sales and customer satisfaction.
Discover grocery purchase patterns to boost sales and enhance customer satisfaction using FP-Growth for precise product recommendations.
Market Basket Analysis
A customer segmentation and product recommendation system using RFM Analysis, Market Basket Analysis & Item Based Collaborative Filter
Market Basket Analysis using ECLAT Algorithm
The goal of this project is to analyze transactional data from a grocery store to identify patterns and associations between items frequently bought together
Group Project for Data Science for Business Project (SMU Master of IT in Business)
Cross the PPI products to those customers who currently don't have one.
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Market Basket Analysis, Portugal based, on TripAdvisor Top 100 Tourist Attractions dataset from January 1, 2019 to August 21, 2021.
The project provides the Apriori algorithm and Market Basket Analysis (MBA) to analyze transactional data, generating personalized recommendations based on Support, Confidence, and Lift metrics to enhance customer experience and boost sales.
Applied SAS techniques for data analysis and machine learning in a milestone project. Base SAS Programming and SAS Viya tools were utilized for preprocessing, customer profiling, sales analysis, promotions, supplier evaluation, and customer segmentation. Results were visualized comprehensively.
Three business analytics case studies were undertaken, encompassing market basket analysis, customer segmentation, and campaign management. SAS Visual Data Mining and Machine Learning on SAS Viya was utilized to explore data and provide insights. A comprehensive report addressing both technical and business aspects was delivered.
The project dives into transaction records of an online retail business to uncover hidden relationships between products. The overall goal is a data-driven approach to enhance the customer shopping experience, improve loyalty, boost profitability, tailor marketing strategies, and optimize inventory management via strategic business decisions.
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